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Geospatial Data Science Quick Start Guide

Geospatial Data Science Quick Start Guide

By : Abdishakur Hassan, Jayakrishnan Vijayaraghavan
4 (6)
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Geospatial Data Science Quick Start Guide

Geospatial Data Science Quick Start Guide

4 (6)
By: Abdishakur Hassan, Jayakrishnan Vijayaraghavan

Overview of this book

Data scientists, who have access to vast data streams, are a bit myopic when it comes to intrinsic and extrinsic location-based data and are missing out on the intelligence it can provide to their models. This book demonstrates effective techniques for using the power of data science and geospatial intelligence to build effective, intelligent data models that make use of location-based data to give useful predictions and analyses. This book begins with a quick overview of the fundamentals of location-based data and how techniques such as Exploratory Data Analysis can be applied to it. We then delve into spatial operations such as computing distances, areas, extents, centroids, buffer polygons, intersecting geometries, geocoding, and more, which adds additional context to location data. Moving ahead, you will learn how to quickly build and deploy a geo-fencing system using Python. Lastly, you will learn how to leverage geospatial analysis techniques in popular recommendation systems such as collaborative filtering and location-based recommendations, and more. By the end of the book, you will be a rockstar when it comes to performing geospatial analysis with ease.
Table of Contents (9 chapters)
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Building the model

Let's build the final model using a random forest regressor. A random forest is a universal machine learning technique, that is, it can handle different kinds of data; it could be a category (classification), a continuous variable (regression), or features of any kind, such an image, price, time, post codes, and so on (that is, both structured and unstructured data). It doesn't generally overfit too much, and it is very easy to stop it from overfitting. For these reasons, random forest is a versatile ML technique which we can effectively use to solve our problem.

Validation data and error metrics

Our initial step is choosing a suitable size for validation data. Before delineating the validation...

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Geospatial Data Science Quick Start Guide
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